Privacy Policies and Measurement - The emergence of mobile edge computing (MEC) imposes an unprecedented pressure on privacy protection, although it helps the improvement of computation performance including energy consumption and computation delay by computation offloading. To this end, we propose a deep reinforcement learning (DRL)-based computation offloading scheme to optimize jointly privacy protection and computation performance. The privacy exposure risk caused by offloading history is investigated, and an analysis metric is defined to evaluate the privacy level. To find the optimal offloading strategy, an algorithm combining actor-critic, off-policy, and maximum entropy is proposed to accelerate the learning rate. Simulation results show that the proposed scheme has better performance compared with other benchmarks.